# Time Series Anomaly Detector

Upload CSV or JSON time series data, detect anomalies with Z-Score and IQR methods, and return a chart-backed report

> Canonical page: https://elysiatools.com/en/tools/time-series-anomaly-detector

- **Category:** Data Analysis

- **Keywords:** time series, anomaly, z-score, iqr, csv, trend

## Overview

The Time Series Anomaly Detector is a powerful utility that analyzes CSV or JSON data to identify unusual spikes, drops, or outliers. By applying statistical methods like Z-Score and Interquartile Range (IQR), it automatically flags anomalies and generates a visual HTML report with a rendered chart, making it easy to spot irregular trends in your data.

## Inputs

- **CSV or JSON Input** (textarea): timestamp,value 2026-03-01T00:00:00Z,10 2026-03-02T00:00:00Z,12
- **Data File** (file)
- **Timestamp Column** (text)
- **Value Column** (text)
- **Detection Method** (select)
- **Z-Score Threshold** (number)
- **Seasonality Window** (number)

## When to use

- When you need to quickly identify abnormal spikes or drops in daily metrics like revenue, traffic, or active users.
- When analyzing server logs or monitoring data to pinpoint latency issues or downtime events.
- When cleaning historical datasets to remove or investigate statistical outliers before training machine learning models.

## How it works

- Upload your time series data as a CSV, JSON, or Excel file, or paste it directly into the text area.
- Specify the column names for your timestamps and values, then select your preferred detection method (Z-Score, IQR, or both).
- Adjust the Z-Score threshold and seasonality window to fine-tune the sensitivity of the anomaly detection.
- Generate a comprehensive HTML report featuring an interactive chart and a summary of all flagged anomaly segments.

## Use cases

- E-commerce managers tracking daily sales to detect unexpected revenue drops or viral product spikes.
- DevOps engineers monitoring server CPU usage and latency to identify infrastructure bottlenecks.
- Financial analysts reviewing daily stock prices or transaction volumes to spot irregular trading activities.

## Frequently asked questions

### What data formats are supported?

You can upload CSV, JSON, or Excel files, or paste raw CSV/JSON text directly into the input field.

### What is the difference between Z-Score and IQR?

Z-Score measures how many standard deviations a data point is from the mean, which is great for normally distributed data. IQR uses quartiles to find outliers and is more robust against extreme values.

### How does the seasonality window work?

The seasonality window allows the tool to account for recurring patterns over a specified number of periods, helping to separate normal seasonal fluctuations from true anomalies.

### Can I customize the sensitivity of the detection?

Yes, you can adjust the Z-Score threshold between 1 and 6. A lower threshold flags more points as anomalies, while a higher threshold only flags extreme outliers.

### What does the output report include?

The tool generates an HTML report containing a visual chart of your time series, highlighted anomaly markers, trend slopes, and a summary of contiguous anomaly segments.

## Related tools

- [Time Series Forecast & Seasonality Analyzer](https://elysiatools.com/en/tools/time-series-forecast-seasonality-analyzer): Forecast future periods from CSV or JSON time-series data and inspect trend, seasonal, and residual decomposition in one report
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- [CSV / Excel Diff Tool](https://elysiatools.com/en/tools/csv-excel-diff-tool): Compare two CSV or XLSX datasets and export a PDF report with row, column, and cell-level differences
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## Samples

- [XLS Samples](https://elysiatools.com/en/samples/xls-samples): Sample legacy Excel (XLS) files for binary spreadsheet parsing and import tests
- [XLSX Samples](https://elysiatools.com/en/samples/xlsx-samples): Sample XLSX spreadsheets for worksheet parsing and data extraction testing
- [CSV Samples](https://elysiatools.com/en/samples/csv-samples): Sample CSV files with various data types, sizes, and complexity levels
- [Python Samples](https://elysiatools.com/en/samples/python): Essential Python code examples and Hello World demonstrations

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